The present disclosure relates to systems and methods for acquiring medical data and, more particularly, to imaging of cardiac activation.
Cardiac disease is a significant challenge to public health and a leading killer in the United States, costing more than 270 billion dollars annually in the United States alone. Each year, about 400,000 sudden cardiac deaths are reported in the United States while a major portion of them are induced by ventricular arrhythmias. In clinical practice, anti-arrhythmic medications are usually administered to suppress the life-threatening syndromes. For the medically refractory cases, catheter ablation has become a standard procedure to eliminate the arrhythmias. The success of such catheter ablation relies on information regarding the arrhythmogenesis. Contact and non-contact intra-cardiac mapping technologies have been employed to guide catheter ablative procedures. However, limited by its invasive nature, these approaches are often time consuming and can only map the cardiac electrical activity on the endocardium of a single or only partial ventricular chamber. Therefore, there is a clinical need to develop non-invasive imaging modalities that can image the cardiac electrical activity throughout the 3D myocardial volume. Such clinical information will improve the effectiveness and efficiency of catheter ablation treatment and also help elucidate the mechanisms of ventricular arrhythmias.
Efforts have been made pursuing noninvasive approaches of mapping cardiac electrical activity by solving the inverse problem of electrocardiography (ECG). Moving dipole localization techniques seek to represent whole heart electrical activity with either one or several moving dipoles. Epicardial imaging techniques expand the solution space from few dipole sources to potential distributions over the epicardial surface. Heart surface activation imaging, alternatively, directly solves myocardial activation time on the heart surfaces based on a physiological model. These methods have been shown to provide potentially valuable information noninvasively, although they estimate cardiac electrical activity over the epicardium or the heart surfaces (including epicardial and endocardial surfaces) instead of over the 3D myocardium.
Over the past decade, cardiac electrical imaging approaches considering the whole myocardium have been pursued. Physiological model based methods incorporate a priori knowledge based physiological model into inverse solutions to solve the ECG inverse problem. Recently, a physical-model based 3D Cardiac Electrical Imaging (3DCEI) approach has been developed and validated on various animal models, such as rabbits and canines, in which good concordance was observed with 3D intra-cardiac mapping results. However, the minimum energy based Weighted Minimum Norm (WMN) method employed by 3DCEI limits the spatial-temporal resolution and robustness against non-Gaussian disturbance such as geometrical modeling error and electrode registration error, which can be introduced in realistic scenarios due to limited raw data quality. The electrophysiology-irrelevant minimum energy constraints imposed may become dominant in reconstruction, leading to a smoothed and distorted imaged activation sequence.
Therefore, the need remains for new and improved non-invasive imaging modalities that can image the cardiac electrical activity throughout the 3D myocardial volume.
Similarly, efforts have been pursued for minimally invasive cardiac imaging using recordings made by catheter. However, there is a need to improve catheter based activation imaging throughout the 3D myocardial volume.
In parallel to electrocardiographic imaging, efforts have also been made to image cardiac electrical activity from magnetocardiographic recordings made out of torso. However, high resolution activation imaging from magnetocardiography (MCG) has been challenging.